Community Size-Structure of the Northwest Atlantic Groundfish Communities, Response to Direct Disturbance and a Changing Environment

Results from a Individual Size Distribution Analysis of the Northeast US Groundfish Community

Author
Affiliation
Adam A. Kemberling

Gulf of Maine Research Institute

Published

December 14, 2022

Potential Journals:

  1. ICES Journal of Marine Science

Abstract

The surface waters of the Northwest Atlantic Ocean are among the fastest warming on Earth. This area is highly-productive biologically, and there are concerns that ecological consequences will follow this rapid-warming. Research on the impacts of this rapid warming has primarily focused on high-profile and/or upper trophic level species. Ecological theory and laboratory studies suggest that elevated temperatures facilitate early maturation and smaller adult body-sizes. However, it is unclear whether that relationship might be mitigated against through adaptive behaviors in an open ocean environment. Here we’ve investigated ecosystem wide impacts on the individual size distribution (ISD) to track changes in community size structure. In cases where community responses are not adequate to counter the impacts of elevated temperatures, we anticipated a steepening of the body-size spectrum slope (ISD exponent). A steeper relationship relating to a reduction in larger sized individuals and an increased prevalence of smaller sized individuals. Using data from fisheries independent surveys we calculated the community size spectra for four regions along the US NE continental shelf. Correlation/regression analyses were then performed to then assess the degree to which these changes were in alignment to hypothesized bottom-up and top-down disturbances. At the regional scale, we found that community size structure changes (spectra slope) were the largest in the Northern regions, in the Gulf of Maine and Georges Bank. These areas are home to the coldest temperatures and the largest proportions of groundfish species in the community. Spectrum slope declines were most pronounced in the 80’s and 90’s, before the rapid warming of the last decade. The timing of these declines suggest that external factors drove the initial declines of larger-sized individuals within the communities, before elevated temperatures began to influence the ecosystem. Correlation analyses reveal that while fisheries landings are strongly correlated with these declines, bottom-up factors of zooplankton community metrics, Gulf Stream Index, and SST anomalies are also important. While the primary pressure of fisheries exploitation has declined dramatically over time, the recovery of larger-sized individuals has not been seen. That kind of recovery will likely depend on the elevated temperatures seen over the last decade.

Introduction

Temperature & Ecology

Temperature plays a critical role on biological life impacting many of the chemical reactions that underpin basic physiological function. Temperature has direct and indirect impacts on critical biological functions including the acquisition of biomass through feeding, the rates of biomass loss through metabolism, and the rates at which individuals mature and develop. Because of these relationships, most species have evolved thermal preferences around which these chemical reactions are optimized. Species that are unable to maintain their thermal preferences internally must be able to follow their thermal preference in the environment through locomotion or adapt to less-favorable conditions through changes in behavior or risk metabolic costs in failing to do so. In an era of anthropogenic climate change, there is an expectation that many species will be displaced from historic habitats in their efforts to follow their thermal preferences. Recent research in marine environments has shown evidence of this as species are now shifting to higher latitudes and to deeper depths in the pursuit of more favorable conditions (Kleisner et al. (2017); Pinsky et al. (2013)). Others have suggested that temperature related impacts may not be seen through geographic distribution change, but through physiological changes, changes in seasonal phenology, or in dashed hopes of species recovery (Daan et al. (2005); Miller et al. (2018); Pershing et al. (2018); University of South Carolina et al. (2021)).

Need to connect temperature to size here

The potential for elevated temperatures to impact the size structure of an ecosystem has implications for the ecosystem resilience in the face of climate change, as well as the blue economies & natural resource systems that rely upon their good health.

Size Spectra and Individual Size Distributions

Size is a defining characteristic of species and mediates many ecological interactions and metabolic pathways (Brown et al. (2000)) . Size is a big factor in determining the mobility of an organism. Mobility then impacts the ability to evade predation, foraging success, efforts to locate and follow essential habitats, geographic ranging, and the metabolic costs associated with each of these behaviors (Hillaert et al. (2018)). Body size also mediates vulnerability to aspects the immediate environment such as temperature through heat exchange or the threat of desiccation in terrestrial species (Gillooly et al. (2001); Heatwole et al. (1969)). Body size even informs life history features like life span and the trophic position an individual might occupy through its impact on metabolism and resource use (White et al. (2007)).

Size structured environments are a fundamental organizational pattern globally that emerges from these relationships add_citation. Within strongly size-structured ecosystems, growth and maturity changes alter fitness and ultimately determine whether a species is successful in that environment add_citation . Ecological theory is rich with models relating how energy transfers from smaller prey species to larger predatory trophic levels, the allocation of energy for growth, and the trade offs of allocating energy towards those ends (Bertalanffy (1938); Bertalanffy (1957); add_citations). A globally persistent pattern in ecology entangled in those relationships and their critiques is the decline in abundance with increasing body size (Damuth (1981); Currie (1993); Sheldon et al. (1972); Loeuille and Loreau (2006)).

This relationship, between size and abundance, integrates multiple processes operating on the cellular, individual, and community levels simultaneously. The quantities for size and abundance are also some of the most readily collected data assets of any ecological community. This creates an opportunity to learn much about a system from a relatively low-effort in data collection. For these reasons, size spectrum analyses and individual size distribution (ISD) methods have gained increasing attention as an entry point to assessing ecosystem health and to detect system-wide disturbance (Shin et al. (2005)‘; Pomeranz et al. (2022); White et al. (2007)). An advantage of these models is they avoid the need to explicitly articulate each predator-prey interactions as they and can be estimated from the commonly collected measures of abundance and size. The “size spectrum” describes the distribution of biomass or abundance as a function of individuals’ mass or size on a log–log scale (Guiet et al. (2016); Kerr and Dickie (2001)) . Size spectra are described by two terms, the size spectrum slope & intercept. These two terms reveal a sense of the baseline productivity, and how energy flows through an ecosystem (in the form of biomass) from many smaller individuals to many fewer large individuals. The spectrum intercept has been linked to the productivity of the community, and is often connected to the prevailing environmental conditions (Boudreau and Dickie (1992); Rossberg (2012)).

How are they used in practice:

Size spectra condense the complexities of predator prey networks and their interactions into a handful of size-based indices. These indices capture the emergent properties of a system, and have become increasingly used as indicators of ecosystem health. Within the context of fisheries management, changes in specctrum slopes have been associated with fishing exploitation, primarily through the targeted removal of larger individuals (Bianchi et al. (2000); Shin et al. (2005)). Numerical experiments have also linked changes in slope to environmental disturbances (Guiet et al. (2016)). Biomass spectra have also been shown to express predictable relationships between ecosystems of similar productivity levels as well as from distinct temperatures (Guiet et al. (2016)).

Direct quote from Guiet et al. (2016) , but nails the connection back to temp expectations:

Because it controls chemical reactions, temperature controls metabolic rates which underpin maintenance, growth or reproduction (Clarke and Johnston, 1999; Kooijman, 2010) as well as the functional responses to food density (Rall et al., 2012). Guiet et al. (2016)… In addition to the impact of temperature on communities’ intercepts (heights), the impact of temperature on the speed of the energy flow within communities may affect other properties, such as their resilience to perturbations or the intensity of trophic cascades (Andersen and Pedersen, 2009).

Use Pomerantz paper & Edwards to extend into ISD

Temperature of the Gulf of Maine & NE Shelf

In addition to the ecological disturbances of industrial fisheries, the Northwest Atlantic is also one of the fastest warming locations in the global oceans. Sea surface temperatures in the Gulf of Maine since 1982 have been warming at rates faster than 96% of the world’s oceans, with similar warming rates along the northwest Atlantic shelf (Pershing et al. (2018)). This persistent elevated temperature regime of the area is a result of several forces, a combination of shifting ocean currents and the unique bathymetry of the region. A Northward shift in the Gulf Stream directly increased the regional temperatures through increased transport of warm Gulf Stream water into areas like the Gulf of Maine. The Northward Gulf Stream shift is associated with a higher frequency of warm core rings, and the obstruction of cold-water Scotian Shelf current flow that would otherwise counter the influence of the Gulf Stream on the region’s temperatures (Gangopadhyay et al. (2019); University of South Carolina et al. (2021)). The combination of these oceanographic changes has led to a warmer continental shelf habitat.

The rapid warming in the northwest Atlantic is a major factor in the redistribution of marine species along the US east coast. Species have responded by adjusting the timing and locations of their seasonal migrations and shifting their geographic ranges (Nye et al. (2009); Staudinger et al. (2019)). There is evidence that warming has hampered fisheries recoveries as well. Adding a metabolic tax to physiological pathways like growth and metabolism. Species like Black Sea Bass, Atlantic shortfin squid, and Blue crab have been high-profile examples of species expanding their ranges to follow their thermal preferences. While species like the American lobster have shown declines at their southern range near Long Island Sound, with much doubt whether they will recover under the present temperature trends. The recent regime shift in the physical oceanography has also shown to be a catalyst for biological shifts as well (University of South Carolina et al. (2021); Perretti et al. (2017)).

While these examples show that species can respond to changes in the physical environment around them through movement & behavior, research elsewhere suggests that physiological responses integrated across species will manifest as changes in community size structure.

Purpose

With the understanding that populations depend on the health of their ecosystems, there is a need to have community-wide metrics to effectively understand and manage marine resources. Size based indices are metrics that can be estimated from the information that has historically been available from long-term survey efforts. These indices have been shown to be sensitive to the impacts of fishing, but should also capture environmentally driven stress as well. We estimated size spectrum relationships as SBI’s for the groundfish populations for each sub-region of the Northeast US continental shelf. In the case of the NW Atlantic sustained increases in temperature should have a physiological impact on the community size structure.

This leads to our second hypothesis:

H2. Warming alters the community through the direct influence of temperature on metabolism, growth, and population productivity.

Methods

Groundfish Data

Fishery Independent data on was collected as part of the NEFSC’s northeast trawl survey. This survey is conducted each year in the spring and in the fall, with sample locations determined following a stratified-random survey design with effort allocated in proportion to stratum area. Trawls are performed for a fixed duration at each station, reporting abundance at length and total biomass for all species caught. Trawl survey analyses incorporated data from both the Spring and Fall survey seasons, and for all years from 1970 to 2019. Correction factors were applied to total species abundance and aggregate species biomass to account for all changes in vessels, gear, and doors when appropriate as part of the survey program. However, abundance and biomass at length is not corrected for as part of the standard survey data protocol and needed to be estimated. To account for this, abundance at length for each species were adjusted to match the correction factors applied to total species abundance at each station, with allocations following the distributions of length caught at that station. Such that for each species: the sum of the resulting adjusted abundance numbers across each length is equal to the total abundance that was corrected for changes in vessels, gear, etc. To account for differences in sampling effort among survey strata, all corrected abundance-at-length data was then area-stratified.

Data from the survey was grouped by strata to form geographically meaningful sub-regions: Gulf of Maine, Georges Bank, Southern New England, Mid-Atlantic Bight. For each region, we developed several time series indicators:

  1. Community Composition metrics (abundance and biomass by functional group, with body-size contributions)

  2. Mean size of the aggregate community and key functional groups

  3. Slope and intercept of the size spectrum

Community Composition

Functional groups were assigned to each species based on life history and geography using the definitions of (Hare et al. (2010)). Functional groups included were coastal, diadromous, elasmobranch, groundfish, pelagic, and reef species.

Body Size Changes

Abundance-weighted body length and body weight within each region and for each functional group were also estimated using the numbers-at-length and their estimated biomass-at-length information. Data for body size trends were not truncated using any minimum or maximum size. No area-stratification or other weighting was applied for body-size change estimates.

Size Distribution Analyses

Community size spectra relationships were estimated using the abundance-at-length data, and included 68 species. These species were selected based on the availability of published weight-at-length relationships (Wigley et al. 2003) and represented 98.98% of the total biomass caught in the survey. Published length-weight relationships were used to convert length data available for all individuals into their corresponding biomass-at-length. These weight estimates were then used to get area-stratified weight-at-length values. A biomass complement to the abundances-at-length data which had been area stratified. These area-stratified biomass values were then used when estimating the biomass size spectra and exponents of individual size distributions for each region. Data for these analyses was truncated by body masses of a minimum 1g and a maximum 2^13g to account for poor gear selectivity at the smallest and largest size ranges effectively sampled by the gear. This was done to ensure that all size bins for the size body mass spectrum fitting were not empty in any years, and to establish minimum and maximum size bounds for the individual size distribution relationship that were supported by the data collected.

Body Mass Spectra

Normalized biomass spectra were estimated following the LBNbiom methodology as described in Edwards et al. (2017). When fitting the normalized biomass size spectra, stratified biomass at length data was binned into equal spaced intervals on a (1) on a \(log_{2}\) scale, with bodymass totaled across all species. To normalize the spectra, the stratified abundance within each bin was then divided by the bin-width to account for the increasing bin-widths, a consequence of the log scale. Normalized biomass spectra were fit for each year and for each region independently, and for each year across all strata, using a regression of log10( area-stratified abundance, normalized by log2 bin widths ) and the log10( body-size binmidpoints ).

Individual Size Distribution

Length for individuals in the catch data are measured to the nearest cm, with smaller specimens measured to the nearest millimeter. Because individual biomass is estimated from those length measurements, there is a range of possible bodymass that spans between the cm & mm measurement increments. The relationships between length and bodymass in fishes is exponential and taxon specific, so biases resulting from using only the lower or upper end of those ranges is different for each taxon and increases for larger taxa. To account for this and reduce biases, we used the extended likelihood method (MLEbin) of Edwards et al. (2020) . This method estimates the exponent of size spectra (b) for a bounded power law relationship between abundance and their length-estimated biomass.

\[ \begin{align*} f(x) = \frac{ (\lambda + 1)x^{\lambda} }{ x^{\lambda+1}_{max} - x^{\lambda+1}_{min} }~~~~~~\lambda\neq1, \\ \\ f(x) = \frac{1}{logx_{max} - logx_{min}}~~~~~~\lambda=1 \end{align*} \tag{1}\]

The individual size distribution relationship was estimated for each survey region and for all years from 1970 to 2019. A minimum biomass of 1g was used for the lower bound (\(x_{min}\)) and a maximum biomass of 10kg was used as an upper bound (\(x_{max}\)) for the ISD’s bounded power law probability density function Equation 1, where \(x\) is body mass & \(\lambda\) is the scaling exponent of the ISD. The biomass within these limits represents 97.83% of all estimated biomass used in these analyses and was chosen to eliminate sizes that were not well sampled by the survey gear. Exponents of size spectra were calculated using code modified from the sizeSpectra package in R (Edwards et al. (2017); Edwards et al. (2020)).

Sea Surface Temperatures

Global Sea surface temperature data was obtained via NOAA’s optimally interpolated SST analysis (OISSTv2), providing daily temperature values at a 0.25° latitude x 0.25° longitude resolution (Reynolds et al. 2007). A daily climatology for every 0.25° pixel in the global data set was created using average daily temperatures spanning the period of 1982-2011. Daily anomalies were then computed as the difference between observed temperatures and the daily climatological average. OISSTv2 data used in these analyses were provided by the NOAA PSL, Boulder, Colorado, USA from their website at https://www.psl.noaa.gov.

Sea surface temperature data was regionally averaged to match the survey regions from the age-at-length data. SST anomalies were averaged by year for each region and over the entire sampling region to produce daily time series. These time series were then processed into annual timeseries of surface temperatures and anomalies. All region-averaging was done with area-weighting of the latitude/longitude grid cells to account for differences in cell-size in the OISSTv2 data.

Drivers of Size Distribution Changes

The impact of external factors on the changes in size spectra was correlated against several hypothesized driving forces related to both environmental regimes and anthropogenic disturbances. Potential large-scale environmental drivers include sea surface temperature anomalies & the Gulf Stream Index (GSI). The primary top-down drivers include state and federal fisheries landings from the Greater Atlantic Regional Fisheries Office (GARFO), divided by reporting zones into aggregate regions to closely align with the survey areas we defined for the size spectra analyses.

Driver Impacts

Regression analyses were used to compare the impact of each driver over time.

Results

Community Abundance

Stratified abundance was highest in the Gulf of Maine, and decreased across regions moving from North to South. Abundance across all body sizes remained relatively stable in all four regions until the 1990’s. At this time abundance in the Gulf of Maine began to steadily rise. This increase in abundance reversed briefly from 2005-2010, but resumed and continued to rise until its peak in 2016. Georges Bank abundances remained low and stable until after 2008, when numbers rapidly increased through 2014, before quickly falling back to numbers slightly above normal by the end of the decade. Abundances in Southern New England experienced higher inter-annual changes in abundance across all years. This area saw a less dramatic rise and fall that began just before 2007, again falling back to earlier levels by the end of the decade. The Mid Atlantic Bight displayed the most inter-annual variability and had relatively consistent abundances throughout, with no major periods of abundance growth or decline.

Abundance gains observed in Georges Bank and Gulf of Maine were primarily from groundfish species, with additional growth from diadromous species seen in the Gulf of Maine. Increases in abundance across all areas was mostly attibutable to individuals weighing less than .5kg. With some years driven in large-part by exceptional year-classes in just a handful of species e.g. haddock in Georges Bank. The observed abundance volatility in Southern New England and the Mid-Atlantic Bight conversely was largely the result of changes in abundance in pelagic species, whose abundance varied by several times the magnitude that of the other functional groups.

Community Biomass

Similar to abundance, the overall biomass was highest in the two northern regions, the Gulf of Maine and Georges Bank. Roughly half of the biomass sampled in these regions can be attributed to groundfish species, with the second largest contributions coming from elasmobranchs. Within the groundfish biomass, larger individuals >2kg in particular, declined during the 70’s and 80’s in these regions, never truly recovering. Beginning in the 2000’s there were signs that groundfish abundances were increasing as evidenced by increasing numbers of smaller individuals, however in both regions this trend appears to have reversed by the mid 2010’s. Elasmobranch biomass increased steadily throughout the survey time period across all regions, with the exception of southern New England. This area showed large 5-10 year swings in biomass, but no clear long-term trend. Larger elasmobranch were rare in all regions except for a period spanning the late 70’s through the early 90’s isolated to Georges Bank. Demersal species biomass was highest in the Gulf of Maine, dwarfing their contributions in other regions. Their biomass declined in the 70’s, was flat until the late 90’s, remaining relatively high until declining in the late 2010’s. Pelagic species biomass was low in all regions, and is unlikely to be representative of true biomass trends due to gear selectivity.

Regional Variation in Species Composition

There was a distinct difference between Northern and Southern regions in the way biomass was distributed among the different functional groups. The largest contributors to biomass in the southern regions (southern New England & mid-Atlantic bight) was the elasmobranch community. While the northern regions (Gulf of Maine & Georges Bank) each had similar quantities of elasmobranch biomasses, there was also a comparable contribution of groundfish and in the Gulf of Maine there was a major component of demersal species as well.

Body Size Trends

The average fish size in the Gulf of Maine (length and weight) declined the greatest of all regions over our study period. The average individual length was greatest in the 1970’s in the 35-40cm range, falling to 28-33cm over the last decade. Body-weight fell dramatically in the 1980’s, from around .75kg in the 1970’s to .25-.30kg, roughly a third of what it had been. Georges Bank body sizes also declined during the study period, but less dramatically. Both of these Northern regions had brief period in the late 2000’s where average length and weight rose, before falling again in the 2010’s. The MAB region was the only region to see a long-term increase in both length and weight during the study period. SNE saw no long-term change in length, and a minor decline in average body-weight.

Regional Size Spectra

At the start of our time series, back in the 1970’s, there was a clear difference in the relative positions of spectra parameters among the different regions. Gulf of Maine and Georges Bank showed the least steep spectra slopes in the earlier time periods with slopes around -1 & -1.1 respectively. The relatively flat slopes in these regions both steepened over time, settling near -1.3 (GoM) and -1.5 (GB). Gulf of Maine experienced much of its decline during the 1980’s and 1990’s. There was a brief reversal in this trend during the 2000’s, but slopes continued to steepen by 2010 and remained steep through 2019. Georges Bank did not experience as rapid of a decline, but experienced a similar long-term steepening. In contrast to the northern regions, SNE and MAB had steeper slopes in the -1.2 to -1.5 territory. The long term pattern for SNE was one of increasing volatility, but not so much a decline. The spectra slope for the MAB was less volatile, but similarly maintained a relatively stable wander around -1.4. By the end of the study period all regions had slopes that were at or near a similar level.

Size Spectra Drivers

Driver Correlations

NOTE: Correlation matrix is computed starting at the year where there are no NA values in any drivers. Currently with SST included that begins the matrix at 1982.

Discussion

  • Top-down and bottom up influences on both carrying capacity (intercept) and transfer efficiency (slope)

Some of the major drivers suggested here operate on both, but to varying degrees. Here are some potential mechanisms:

Literature suggests: - Intercept (a proxy for productivity and carrying capacity) is primarily determined by bottom up features like: nutrient availability, temperature

  • Slope (a measure of energy transfer efficiency and static biomass distribution) has been shown to be sensitive to the physical removal of species through fishing.

Temperature Mechanisms: - Temperature’s impact on growth via genetic plasticity impacts both the available biomass at the primary producer level (impacting ecosystem carrying capacity), as well as the Linf of larger species (recruitment rate). - Temperature also impacts the efficiency of energy (as biomass) being transferred between individuals via predator & prey interactions. More energy per-capita is expended in the form of increased metabolic rates and/or behavioral changes. This metabolic tax should steepen the spectrum slope by removing available energy at a system wide level. - Temperature impacts behavior via physiological impacts on metabolism and foraging rates as well as through the avoidance of temperature stresses.

Separating Complimentary Forces Impacting Growth

The data used for this analysis was collected as part of a survey program which began out of concern that fisheries were already being over-harvested. Early estimates from scientists at that time suggested that by the 1970’s total biomass of Georges Bank had already been halved, and elasmobranch species had begun to replace the over-harvested gadoid species (Fogarty and Murawski, 1998). Having such a large disturbance which pre-dates our time series is suggestive that the measured steepening of size spectrum slope we observed in this area and the adjacent Gulf of Maine are potentially the tail-ends of a longer and more severe ecosystem decline. While metrics of overall fishing pressure do not align exactly with trawl survey coverage, historical records and anecdotal evidence fro that time suggest that groundfish fishing pressure in these areas are a fraction of their what their impacts were in the 1960’s and 1970’s.

Forces Preventing the Recovery of Large Individuals

This begs the question of why larger adult numbers never began to recover in these regions. Looking at abundance and biomass information from the survey there was evidence of strong recruitment among smaller individuals < 1kg, but there has since not been a recovery in fishes larger than 1kg outside of the elasmobranchs. Work by (Pershing et al., 2015) suggested this prolonged recovery period may be due to a lack of accounting for temperature change in fisheries management. At the time of that research, the regional temperatures had only begun rising, and could have been considered at that time an acute stressor. Since that time the region has experienced nearly a decade of sustained above-average temperatures. There are signs that the success seen in recruitment and survival of even the smaller size classes is declining. While temperature change has been associated with changes in growth rates and size-at-age, so too have size-selective fishing practices, making it difficult to disentangle the importance of exploitation & temperature on the overall community size structure when body size integrates these two forces (Shackell and Frank, 2007).

Potential Drivers Timeseries:

Index of What

Our ability to make statements on community size-structure change in this study is limited by the community that is effectively sampled as part of the groundfish survey, and of species with available weight-at-length information. When looking at the species compositions in each region it is clear that in SNE and MAB a larger proportion of the community biomass was held within a relatively small number of species, primarily the elasmobranchs. Whether or not this is incomplete representation of the community due to the sampling gear, or an accurate representation of the entire community is unclear. In these two regions the biomass spectra information is largely that of the elasmobranch community. Whether or not this is a sufficient fraction of the broader community, what key community members are absent, and what the ecological implications of this are remain unclear.

Looking at both the level of noise in the body-size trends and the size spectra parameters, the Gulf of Maine and Georges Bank seem to have the clearest signals coming through. It is likely not coincidental that these two regions also have large proportions of their sampled biomass represented by groundfish and demersal species, functional groups that are best sampled by a bottom trawl survey. Looking specifically at these two regions only, we see two parallel trends of declining body size (length and weight) among the community as a whole. We also simultaneously see a steepening of the size spectra slope. Both of these trends are slightly more severe in the Gulf of Maine.

Supplemental Materials

Functional Group Assignments and Regional Presence/Absence
Common Name Gulf of Maine Georges Bank Southern New England Mid-Atlantic Bight
Coastal
Atlantic Croaker X X X
Atlantic Thread Herring X X
Blueback Herring X X X X
Bluefish X X X X
Butterfish X X X X
Northern Kingfish X X X
Southern Kingfish X
Spanish Mackerel X
Spanish Sardine X
Spot X X
Striped Bass X X X X
Weakfish X X X
Elasmobranch
Atlantic Angel Shark X
Atlantic Sharpnose Shark X
Barndoor Skate X X X X
Bullnose Ray X X
Chain Dogfish X X X
Clearnose Skate X X
Cownose Ray X
Little Skate X X X X
Rosette Skate X X X X
Roughtail Stingray X
Sand Tiger X
Sandbar Shark X X
Smooth Butterfly Ray X
Smooth Dogfish X X X X
Smooth Skate X X X X
Spiny Butterfly Ray X
Spiny Dogfish X X X X
Thorny Skate X X X X
Winter Skate X X X X
Groundfish
Acadian Redfish X X X X
American Plaice X X X X
Atlantic Cod X X X X
Atlantic Halibut X X X
Atlantic Wolffish X X X
Cusk X X X X
Fawn Cusk-Eel X X X X
Fourspot Flounder X X X X
Goosefish X X X X
Haddock X X X X
Longhorn Sculpin X X X X
Northern Searobin X X X X
Ocean Pout X X X X
Offshore Hake X X X X
Pollock X X X X
Red Hake X X X X
Sea Raven X X X X
Silver Hake X X X X
Spotted Hake X X X X
Summer Flounder X X X X
White Hake X X X X
Windowpane Flounder X X X X
Winter Flounder X X X X
Witch Flounder X X X X
Yellowtail Flounder X X X X
Pelagic
Atlantic Herring X X X X
Atlantic Mackerel X X X X
Buckler Dory X X X X
Round Herring X X X X
Reef
Atlantic Spadefish X
Black Sea Bass X X X X
Blackbelly Rosefish X X X X
Cunner X X X X
Greater Amberjack X X
Scup X X X X
NA
American Shad X X X X
Atlantic Sturgeon X
Functional group assignments adapted from Hare et al. 2010
Top Commercial Fisheries Landings of Northeastern US (by weight)
Avg. Annual Landings (lb.) Total Landings (lb.) Total Value ($)
Gulf of Maine - 1960
Hake, Silver 16.58M 281.87M 8.71M
Herring, Atlantic 11.57M 138.83M 2.50M
Redfish, Acadian 2.12M 88.97M 3.41M
Gulf of Maine - 1970
Herring, Atlantic 22.78M 501.08M 19.70M
Menhaden, Atlantic 17.78M 373.48M 7.87M
Redfish, Acadian 3.14M 219.85M 23.87M
Gulf of Maine - 1980
Herring, Atlantic 21.78M 653.26M 34.52M
Menhaden, Atlantic 21.24M 509.75M 12.77M
Pollock 3.33M 229.57M 62.00M
Gulf of Maine - 1990
Herring, Atlantic 25.21M 958.12M 54.12M
Cod, Atlantic 2.35M 138.76M 131.76M
Shark, Dogfish, Spiny 3.34M 120.17M 15.95M
Gulf of Maine - 2000
Herring, Atlantic 2.99M 47.77M 4.31M
Monkfish/Angler/Goosefish 716.21K 31.51M 51.13M
Cod, Atlantic 692.95K 30.49M 42.30M
Gulf of Maine - 2010
Tuna, Bluefin 209.06K 3.76M 33.30M
Shark, Dogfish, Spiny 479.11K 2.87M 590.62K
Pollock 188.20K 1.69M 2.08M
Georges Bank - 1960
Haddock 15.00M 270.06M 34.41M
Hake, Silver 6.83M 95.57M 3.19M
Cod, Atlantic 4.88M 87.89M 8.12M
Georges Bank - 1970
Cod, Atlantic 7.78M 233.48M 59.16M
Flounder, Yellowtail 4.62M 138.52M 43.16M
Redfish, Acadian 2.63M 76.37M 9.09M
Georges Bank - 1980
Cod, Atlantic 10.11M 404.40M 211.60M
Flounder, Winter 2.50M 100.11M 89.84M
Haddock 2.36M 94.27M 66.68M
Georges Bank - 1990
Cod, Atlantic 4.27M 192.29M 190.26M
Hake, Silver 1.79M 76.82M 20.49M
Flounder, Winter 1.23M 56.43M 75.59M
Georges Bank - 2000
Cod, Atlantic 2.17M 62.91M 75.20M
Herring, Atlantic 3.49M 48.92M 3.73M
Haddock 1.54M 43.01M 55.37M
Georges Bank - 2010
Hake, Silver 155.88K 779.40K 499.90K
Haddock 39.65K 118.95K 143.04K
Flounder, Winter 40.40K 80.80K 216.28K
Southern New England - 1960
Other Fish, Bony 14.84M 400.77M 3.73M
Flounder, Yellowtail 6.56M 196.83M 19.12M
Flounder, Winter 2.52M 70.58M 7.01M
Southern New England - 1970
Menhaden, Atlantic 9.99M 239.84M 5.12M
Other Fish, Bony 4.05M 206.59M 2.49M
Flounder, Yellowtail 2.07M 153.55M 36.47M
Southern New England - 1980
Menhaden, Atlantic 6.60M 217.68M 10.21M
Hake, Silver 2.56M 205.02M 46.11M
Flounder, Yellowtail 1.66M 132.92M 83.38M
Southern New England - 1990
Hake, Silver 2.52M 196.81M 78.54M
Herring, Atlantic 2.12M 129.02M 7.19M
Menhaden, Atlantic 3.71M 125.98M 8.69M
Southern New England - 2000
Mackerel, Atlantic 2.55M 135.06M 15.60M
Hake, Silver 1.00M 55.25M 26.89M
Skate, Nk 950.56K 49.43M 6.53M
Southern New England - 2010
Scup 161.10K 6.44M 4.29M
Hake, Silver 145.07K 4.21M 3.12M
Flounder, Summer 80.43K 3.86M 11.54M
Mid-Atlantic Bight - 1960
Flounder, Summer 2.03K 4.05K 720.00
Flounder, Yellowtail 2.33K 2.33K 214.00
Flounder, Witch 395.00 395.00 36.00
Mid-Atlantic Bight - 1970
Menhaden, Atlantic 10.20M 50.98M 1.59M
Weakfish/Sea Trout, Squeteague 886.91K 9.76M 1.40M
Scup 876.60K 8.77M 2.09M
Mid-Atlantic Bight - 1980
Menhaden, Atlantic 30.78M 646.41M 10.94M
Flounder, Summer 1.15M 83.83M 72.00M
Scup 550.89K 37.46M 15.53M
Mid-Atlantic Bight - 1990
Menhaden, Atlantic 115.86M 4.63B 286.14M
Mackerel, Atlantic 1.67M 103.62M 13.87M
Croaker, Atlantic 1.35M 71.65M 22.53M
Mid-Atlantic Bight - 2000
Menhaden, Atlantic 69.60M 2.64B 167.17M
Croaker, Atlantic 2.16M 106.02M 42.93M
Mackerel, Atlantic 1.70M 59.41M 6.38M
Mid-Atlantic Bight - 2010
Menhaden, Atlantic 118.29M 1.89B 154.46M
Bass, Striped 1.70M 25.56M 75.05M
Croaker, Atlantic 1.08M 24.81M 21.37M
Landings data obtained from the Greater Atlantic Regional Fishing Office (GARFO)

References

Bertalanffy, L. von. 1938. A quantitative theory of organic growth (inquiries on growth laws. II.). Human Biology, 10: 181213. https://www.proquest.com/docview/1301827069/citation/94DE10A3AF8F4DF7PQ/1.
Bertalanffy, L. von. 1957. Quantitative laws in metabolism and growth. The Quarterly Review of Biology, 32: 217–231. https://www.journals.uchicago.edu/doi/abs/10.1086/401873.
Bianchi, G., Gislason, H., Graham, K., Hill, L., Jin, X., Koranteng, K., Manickchand-Heileman, S., et al. 2000. Impact of fishing on size composition and diversity of demersal fish communities. ICES Journal of Marine Science, 57: 558–571. https://doi.org/10.1006/jmsc.2000.0727.
Boudreau, P. R., and Dickie, L. M. 1992. Biomass spectra of aquatic ecosystems in relation to fisheries yield. Canadian Journal of Fisheries and Aquatic Sciences, 49: 1528–1538. https://cdnsciencepub.com/doi/abs/10.1139/f92-169.
Brown, J., West, G. B., and Enquist, B. 2000. Scaling in biology: Patterns and processes, causes and consequences. In pp. 1–24.
Currie, D. J. 1993. What shape is the relationship between body size and population density? Oikos, 66: 353. https://www.jstor.org/stable/3544825?origin=crossref.
Daan, N., Gislason, H., G. Pope, J., and C. Rice, J. 2005. Changes in the North Sea fish community: evidence of indirect effects of fishing? ICES Journal of Marine Science, 62: 177–188. https://academic.oup.com/icesjms/article/62/2/177/602590.
Damuth, J. 1981. Population density and body size in mammals. Nature, 290: 699–700. http://www.nature.com/articles/290699a0.
Edwards, A., Robinson, J., Blanchard, J., Baum, J., and Plank, M. 2020. Accounting for the bin structure of data removes bias when fitting size spectra. Marine Ecology Progress Series, 636: 19–33. https://www.int-res.com/abstracts/meps/v636/p19-33/.
Edwards, A. M., Robinson, J. P. W., Plank, M. J., Baum, J. K., and Blanchard, J. L. 2017. Testing and recommending methods for fitting size spectra to data. Methods in Ecology and Evolution, 8: 57–67. https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.12641.
Fogarty, M. J., and Murawski, S. A. 1998. Large-Scale Disturbance and the Structure of Marine Systems: Fishery Impacts on Georges Bank. Ecological Applications, 8: S6–S22. https://onlinelibrary.wiley.com/doi/abs/10.1890/1051-0761%281998%298%5BS6%3ALDATSO%5D2.0.CO%3B2.
Gangopadhyay, A., Gawarkiewicz, G., Silva, E. N. S., Monim, M., and Clark, J. 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream. Scientific Reports, 9: 12319. http://www.nature.com/articles/s41598-019-48661-9.
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M., and Charnov, E. L. 2001. Effects of Size and Temperature on Metabolic Rate. Science, 293: 2248–2251. https://www.science.org/doi/10.1126/science.1061967.
Guiet, J., Poggiale, J.-C., and Maury, O. 2016. Modelling the community size-spectrum: recent developments and new directions. Ecological Modelling, 337: 4–14. https://www.sciencedirect.com/science/article/pii/S0304380016301958.
Hare, J. A., Alexander, M. A., Fogarty, M. J., Williams, E. H., and Scott, J. D. 2010. Forecasting the dynamics of a coastal fishery species using a coupled climatepopulation model. Ecological Applications, 20: 452–464. http://doi.wiley.com/10.1890/08-1863.1.
Heatwole, H., Torres, F., Blasini De Austin, S., and Heatwole, A. 1969. Studies on anuran water balanceI. Dynamics of evaporative water loss by the coquí, eleutherodactylus portoricensis. Comparative Biochemistry and Physiology, 28: 245–269. https://www.sciencedirect.com/science/article/pii/0010406X69913425.
Hillaert, J., Hovestadt, T., Vandegehuchte, M. L., and Bonte, D. 2018. Size-dependent movement explains why bigger is better in fragmented landscapes. Ecology and Evolution, 8: 10754–10767. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6262741/.
Kerr, S. R., and Dickie, L. M. 2001. The Biomass Spectrum: A Predator-Prey Theory of Aquatic Production. Columbia University Press.
Kleisner, K. M., Fogarty, M. J., McGee, S., Hare, J. A., Moret, S., Perretti, C. T., and Saba, V. S. 2017. Marine species distribution shifts on the U.S. Northeast Continental Shelf under continued ocean warming. Progress in Oceanography, 153: 24–36. https://linkinghub.elsevier.com/retrieve/pii/S0079661116301896.
Loeuille, N., and Loreau, M. 2006. Evolution of body size in food webs: does the energetic equivalence rule hold? Ecology Letters, 9: 171–178.
Miller, T. J., O’Brien, L., and Fratantoni, P. S. 2018. Temporal and environmental variation in growth and maturity and effects on management reference points of Georges Bank Atlantic cod. Canadian Journal of Fisheries and Aquatic Sciences, 75: 2159–2171. http://www.nrcresearchpress.com/doi/10.1139/cjfas-2017-0124.
Nye, J., Link, J., Hare, J., and Overholtz, W. 2009. Changing spatial distribution of fish stocks in relation to climate and population size on the Northeast United States continental shelf. Marine Ecology Progress Series, 393: 111–129. http://www.int-res.com/abstracts/meps/v393/p111-129/.
Perretti, C., Fogarty, M., Friedland, K., Hare, J., Lucey, S., McBride, R., Miller, T., et al. 2017. Regime shifts in fish recruitment on the Northeast US Continental Shelf. Marine Ecology Progress Series, 574: 1–11. http://www.int-res.com/abstracts/meps/v574/p1-11/.
Pershing, A., Mills, K., Dayton, A., Franklin, B., and Kennedy, B. 2018. Evidence for Adaptation from the 2016 Marine Heatwave in the Northwest Atlantic Ocean. Oceanography, 31. https://tos.org/oceanography/article/evidence-for-adaptation-from-the-2016-marine-heatwave-in-the-northwest-atl.
Pershing, A. J., Alexander, M. A., Hernandez, C. M., Kerr, L. A., Le Bris, A., Mills, K. E., Nye, J. A., et al. 2015. Slow adaptation in the face of rapid warming leads to collapse of the gulf of maine cod fishery. Science, 350: 809–812. https://www.science.org/doi/full/10.1126/science.aac9819.
Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L., and Levin, S. A. 2013. Marine Taxa Track Local Climate Velocities. Science, 341: 1239–1242. https://science.sciencemag.org/content/341/6151/1239.
Pomeranz, J. P. F., Junker, J. R., and Wesner, J. S. 2022. Individual size distributions across North American streams vary with local temperature. Global Change Biology, 28: 848–858. https://onlinelibrary.wiley.com/doi/10.1111/gcb.15862.
Rossberg, A. G. 2012. 6 - A Complete Analytic Theory for Structure and Dynamics of Populations and Communities Spanning Wide Ranges in Body Size. In pp. 427–521. Ed. by U. Jacob and G. Woodward. Academic Press. https://www.sciencedirect.com/science/article/pii/B9780123969927000083.
Shackell, N., and Frank, K. 2007. Compensation in exploited marine fish communities on the Scotian Shelf, Canada. Marine Ecology Progress Series, 336: 235–247. http://www.int-res.com/abstracts/meps/v336/p235-247/.
Sheldon, R. W., Prakash, A., and Sutcliffe Jr., W. H. 1972. The Size Distribution of Particles in the Ocean1. Limnology and Oceanography, 17: 327–340. https://onlinelibrary.wiley.com/doi/abs/10.4319/lo.1972.17.3.0327.
Shin, Y.-J., Rochet, M.-J., Jennings, S., Field, J. G., and Gislason, H. 2005. Using size-based indicators to evaluate the ecosystem effects of fishing. ICES Journal of Marine Science, 62: 384–396. https://doi.org/10.1016/j.icesjms.2005.01.004.
Staudinger, M. D., Mills, K. E., Stamieszkin, K., Record, N. R., Hudak, C. A., Allyn, A., Diamond, A., et al. 2019. It’s about time: A synthesis of changing phenology in the Gulf of Maine ecosystem. Fisheries Oceanography, 28: 532–566. https://onlinelibrary.wiley.com/doi/abs/10.1111/fog.12429.
University of South Carolina, Meyer-Gutbrod, E., Greene, C., Davies, K., and Johns, D. 2021. Ocean Regime Shift is Driving Collapse of the North Atlantic Right Whale Population. Oceanography, 34: 22–31. https://tos.org/oceanography/article/ocean-regime-shift-is-driving-collapse-of-the-north-atlantic-right-whale-population.
White, E. P., Ernest, S. K. M., Kerkhoff, A. J., and Enquist, B. J. 2007. Relationships between body size and abundance in ecology. Trends in Ecology & Evolution, 22: 323–330. https://linkinghub.elsevier.com/retrieve/pii/S0169534707000985.